.................... POLLSTER RATINGS DATASET VISUALIZATIONS ....................¶
-AIM-
TO FIND OUT WHICH POLLSTER WILL CONDUCTS THE BEST POLL CANDIDATE WINNING PREDICTION
Importing Dependencies
In [1]:
import plotly.express as px
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.graph_objects as go
import warnings
warnings.filterwarnings('ignore')
Reading data
In [2]:
df1 = pd.read_excel("POLLSTER RATINGS.xlsx")
In [3]:
df1
Out[3]:
Exploratory Data Analysis
In [4]:
df1.columns
Out[4]:
In [5]:
df1.isnull().sum()
Out[5]:
In [6]:
df1.info()
In [7]:
df1.nunique()
Out[7]:
In [8]:
df1.describe()
Out[8]:
1. Which pollster is having maximum number of polls in each year(2020,2016,2014)?
In [9]:
fig = px.pie(df1.query("Year == 2020"), values="# of Polls", names="Pollster",hover_name="Year",
title='NUMBER OF POLLS vs POLLSTER corresponding YEAR --> 2020',hole=.5)
fig.update_traces(textposition='inside')
fig.update_layout({'paper_bgcolor': 'rgba(222, 993, 1000, 200)'},uniformtext_minsize=10,
uniformtext_mode='hide',font=dict(color="Blue"))
fig.show()
In [10]:
fig = px.pie(df1.query("Year == 2016"), values="# of Polls", names="Pollster",hover_name="Year",
title='NUMBER OF POLLS vs POLLSTER corresponding YEAR --> 2016',hole=.5,)
fig.update_traces(textposition='inside', textfont_color="Black")
fig.update_layout({'paper_bgcolor': 'rgb(0,0,0)'},uniformtext_minsize=10, uniformtext_mode='hide',font=dict(color="white"))
fig.show()
In [11]:
fig = px.pie(df1.query("Year == 2014"), values="# of Polls", names="Pollster",hover_name="Year",
title='NUMBER OF POLLS vs POLLSTER corresponding YEAR --> 2014',hole=.5)
fig.update_traces(textposition='inside')
fig.update_layout({'paper_bgcolor': 'rgb(255,255,160)'},uniformtext_minsize=10, uniformtext_mode='hide',font=dict(color="Black"))
fig.show()
In [12]:
fig = px.bar(df1.query("Year == 2020"),x="Pollster",y="# of Polls", color="Pollster",barmode="group",
animation_frame="# of Polls",animation_group="Pollster",hover_name="Year",
title='NUMBER OF POLLS vs POLLSTER corresponding YEAR --> 2020',text="# of Polls",range_y=[0,777],
range_x=['Winthrop University','SurveyUSA'])
fig.update_layout({'plot_bgcolor': 'rgba(111, 882, 999, 100)','paper_bgcolor': 'rgba(222, 993, 1000, 200)'},
uniformtext_minsize=8, uniformtext_mode='hide',showlegend = False,
xaxis={'categoryorder':'category ascending'})
fig.update_traces(texttemplate='%{text:.3s}', textposition='outside')
fig.show()
In [13]:
fig = px.bar(df1.query("Year == 2016"),x="Pollster",y="# of Polls", color="Pollster",barmode="group",
animation_frame="# of Polls",animation_group="Pollster",hover_name="Year",
title='NUMBER OF POLLS vs POLLSTER corresponding YEAR --> 2016',text="# of Polls",range_y=[0,777],range_x=['University of Florida','SurveyUSA'])
fig.update_layout({'plot_bgcolor': 'rgb(255,0,0)','paper_bgcolor': 'rgb(255,150,150)'},showlegend = False,
uniformtext_minsize=8, uniformtext_mode='hide',font=dict(color="white"),
xaxis={'categoryorder':'category ascending'})
fig.update_traces(texttemplate='%{text:.3s}', textposition='outside')
fig.show()
In [14]:
fig = px.bar(df1.query("Year == 2014"),x="Pollster",y="# of Polls", color="Pollster",barmode="group",
animation_frame="# of Polls",animation_group="Pollster",hover_name="Year",
title='NUMBER OF POLLS vs POLLSTER corresponding YEAR --> 2014',text="# of Polls",range_y=[0,777],range_x=['Castleton State College','SurveyUSA'])
fig.update_layout({'plot_bgcolor': 'rgb(255,153,0)','paper_bgcolor': 'rgb(0,0,0)'},showlegend = False,
uniformtext_minsize=8, uniformtext_mode='hide',font=dict(color="white"),
xaxis={'categoryorder':'category ascending'})
fig.update_traces(texttemplate='%{text:.3s}', textposition='outside')
fig.show()
OBSERVATION-->
From these plottings In 2020 "Rasmussen Reports/Pulse Opinion Research" pollster got highest pollrate(711) ,
and 2016,2014 "SurveyUSA" got highest pollrate (763,722). But every year polling percentage gradually decreases.
---------------------------------------------------------------------------------------------------------------¶
2. What is "American Association for Public Opinion Research’s", "Roper Center for Public Opinion Research’s " response and polls rate?
In [15]:
fig = px.bar(df1, x="NCPP / AAPOR / Roper", y="# of Polls", color="NCPP / AAPOR / Roper",barmode="group",
animation_frame="# of Polls",animation_group="NCPP / AAPOR / Roper",range_y=[0,777],hover_name="Year",
title='NUMBER OF POLLS vs NCPP / AAPOR / ROPER',text="# of Polls")
fig.update_layout({'plot_bgcolor': 'rgba(111, 882, 999, 100)','paper_bgcolor': 'rgba(222, 993, 1000, 200)'},
uniformtext_minsize=8, uniformtext_mode='hide',xaxis={'categoryorder':'category ascending'})
fig.update_traces(texttemplate='%{text:.3s}', textposition='outside')
fig.show()
In [16]:
fig = px.scatter(df1, x="NCPP / AAPOR / Roper", y="# of Polls", color="NCPP / AAPOR / Roper",size_max=100,
animation_frame="# of Polls",animation_group="NCPP / AAPOR / Roper",range_y=[0,777],
size="# of Polls",hover_name="Year",
title='NUMBER OF POLLS vs NCPP / AAPOR / ROPER')
fig.update_layout({'plot_bgcolor': 'rgb(255,204,153)','paper_bgcolor': 'rgb(255,102,0)'},
uniformtext_minsize=8, uniformtext_mode='hide',font=dict(color="white"),xaxis={'categoryorder':'category ascending'})
fig.show()
In [17]:
fig = px.histogram(df1, x="NCPP / AAPOR / Roper", y="# of Polls",color="NCPP / AAPOR / Roper",
animation_frame="# of Polls",animation_group="NCPP / AAPOR / Roper",range_y=[0,777],hover_name="Year",
title='NUMBER OF POLLS vs NCPP / AAPOR / ROPER')
fig.update_layout({'plot_bgcolor': 'rgb(26,76,52)','paper_bgcolor': 'rgb(51,153,102)'},
uniformtext_minsize=8, uniformtext_mode='hide',font=dict(color="white"),xaxis={'categoryorder':'category ascending'})
fig.show()
In [18]:
fig = px.density_heatmap(df1, x="NCPP / AAPOR / Roper",y="# of Polls",range_y=[0,150],
title='NUMBER OF POLLS vs NCPP / AAPOR / ROPER')
fig.update_layout({'plot_bgcolor': 'rgb(128,0,0)','paper_bgcolor': 'rgb(128,0,0)'},
uniformtext_minsize=8, uniformtext_mode='hide',font=dict(color="white"),
xaxis={'categoryorder':'category ascending'})
fig.show()
In [19]:
from plotly import graph_objects as go
fig = go.Figure(layout={'plot_bgcolor':'skyblue','title':'NCPP / AAPOR / Roper response in corresponding YEAR'})
fig.add_trace(go.Funnel(
name = '2020',
orientation = "h",
y = ["no","yes"],
x = [339,57],marker={'color':['#DB7F93','#DB7F93']},
textposition = "inside",
textinfo = "percent total"))
fig.add_trace(go.Funnel(
name = '2016',
orientation = "h",
y = ["no","yes"],
x = [318,54],marker={'color':['#9BD353','#9BD353']},
textposition = "inside",
textinfo = "percent total"))
fig.add_trace(go.Funnel(
name = '2014',
orientation = "h",
y = ["None","yes"],
x = [283,54],marker={'color':['#A44E92','#A44E92']},
textposition = "outside",
textinfo = 'percent total'))
fig.update_layout(font=dict(color="black",size = 15),yaxis={'categoryorder':'category ascending'})
fig.show()
OBSERVATION --> From these plottings most of the response "NCPP / AAPOR / Roper" is 49% of "NO",in 627 polls
and "American Association for Public Opinion Research’s" results not occur in "2014".
In 2020 "yes" 14% - "No" 86%
In 2016 "yes" 15% - "No" 85%
In 2014 "yes" 16% - "No" 84%
--------------------------------------------------------------------------------------------------¶
3. What is "Exclusively Live Caller With Cellphones" response in which polls rate?
In [20]:
fig = px.bar(df1, x="Exclusively Live Caller With Cellphones", y="# of Polls", color="Exclusively Live Caller With Cellphones",
barmode="group",hover_name="Year",
animation_frame="# of Polls",animation_group="Exclusively Live Caller With Cellphones",range_y=[0,400],
title='NUMBER OF POLLS vs EXCLUSIVELY LIVE CALLER WITH CELLPHONES',text="# of Polls")
fig.update_layout({'plot_bgcolor': 'rgb(255,204,1)','paper_bgcolor': 'rgb(255,255,1)'},
uniformtext_minsize=8, uniformtext_mode='hide',
font=dict(color="black"),xaxis={'categoryorder':'category ascending'})
fig.update_traces(texttemplate='%{text:.3s}', textposition='outside')
fig.show()
In [21]:
fig = px.scatter(df1, x="Exclusively Live Caller With Cellphones", y="# of Polls",
color="Exclusively Live Caller With Cellphones",
animation_frame="# of Polls",size_max=100,width=1080, height=600,hover_name="Year",
animation_group="Exclusively Live Caller With Cellphones",range_y=[0,777],size="# of Polls",
title='NUMBER OF POLLS vs EXCLUSIVELY LIVE CALLER WITH CELLPHONES')
fig.update_layout({'plot_bgcolor': 'rgb(204,204,255)','paper_bgcolor': 'rgb(0,0,128)'},
uniformtext_minsize=8, uniformtext_mode='hide',font=dict(color="white"))
fig.show()
In [22]:
fig = px.histogram(df1, x="Exclusively Live Caller With Cellphones", y="# of Polls",
color="Exclusively Live Caller With Cellphones",
animation_frame="# of Polls",animation_group="Exclusively Live Caller With Cellphones",
range_y=[0,777],hover_name="Year",
title='NUMBER OF POLLS vs EXCLUSIVELY LIVE CALLER WITH CELLPHONES')
fig.update_layout({'plot_bgcolor': 'rgb(190,190,190)','paper_bgcolor': 'rgb(150,150,150)'},
uniformtext_minsize=8, uniformtext_mode='hide',font=dict(color="white"),
xaxis={'categoryorder':'category ascending'})
fig.show()
In [23]:
fig = px.density_heatmap(df1, x="Exclusively Live Caller With Cellphones",y="# of Polls",range_y=[0,150],
title='NUMBER OF POLLS vs EXCLUSIVELY LIVE CALLER WITH CELLPHONES')
fig.update_layout({'plot_bgcolor': 'rgb(255,0,255)','paper_bgcolor': 'rgb(255,180,204)'},
uniformtext_minsize=8, uniformtext_mode='hide',font=dict(color="black"),
xaxis={'categoryorder':'category ascending'})
fig.show()
In [24]:
fig = px.box(df1, x="Exclusively Live Caller With Cellphones",y="# of Polls",range_y=[0,380]
,color = "Exclusively Live Caller With Cellphones",
title='NUMBER OF POLLS vs EXCLUSIVELY LIVE CALLER WITH CELLPHONES')
fig.update_layout({'plot_bgcolor': 'rgb(255,255,255)','paper_bgcolor': 'rgb(0,0,255)'},
uniformtext_minsize=8, uniformtext_mode='hide',font=dict(color="white"),
xaxis={'categoryorder':'category ascending'})
fig.show()
OBSERVATION -->From these plottings most of the response "Exclusively Live Caller With Cellphones" is 49% of "YES",in 438 polls because always conducts polls via live interviewers who place calls to cellphones in addition to landlines and it had outliers, "Exclusively Live Caller With Cellphones" results not occur in "2014".
-----------------------------------------------------------------------------------------------------------¶
4. which methodologies a pollster routinely uses in its election polls in campaign cycles ?
In [25]:
fig = px.bar(df1, x="Methodology", y="# of Polls", color="Methodology",barmode="group",
animation_frame="# of Polls",animation_group="Methodology",range_y=[0,777],hover_name="Year",
title='NUMBER OF POLLS vs METHODOLOGY',text="# of Polls")
fig.update_layout({'plot_bgcolor': 'rgb(153,20,102)','paper_bgcolor': 'rgb(153,204,0)'},
uniformtext_minsize=8, uniformtext_mode='hide',
font=dict(color="white"),
xaxis={'categoryorder':'category ascending'})
fig.update_traces(texttemplate='%{text:.3s}', textposition='outside')
fig.show()
In [26]:
fig = px.scatter(df1, x="Methodology", y="# of Polls", color="Methodology",
animation_frame="# of Polls",size_max=100,hover_name="Year",
animation_group="Methodology",range_y=[0,777],size="# of Polls",
title='NUMBER OF POLLS vs METHODOLOGY')
fig.update_layout({'plot_bgcolor': 'rgb(255,255,150)','paper_bgcolor': 'rgb(255,0,0)'},
uniformtext_minsize=8, uniformtext_mode='hide',font=dict(color="white"))
fig.show()
In [27]:
fig = px.histogram(df1, x="Methodology", y="# of Polls",color="Methodology",
animation_frame="# of Polls",animation_group="Methodology",range_y=[0,777],
title='NUMBER OF POLLS vs METHODOLOGY')
fig.update_layout({'plot_bgcolor': 'rgb(224,255,255)','paper_bgcolor': 'rgb(0,128,128)'},
uniformtext_minsize=8, uniformtext_mode='hide',font=dict(color="white"))
fig.show()
In [28]:
fig = px.strip(df1, x="Methodology",y="# of Polls",range_y=[-1,300],hover_name="Year",color = "Methodology",
title='NUMBER OF POLLS vs METHODOLOGY')
fig.update_layout({'plot_bgcolor': 'rgb(255,255,255)','paper_bgcolor': 'rgb(0,255,0)'},
uniformtext_minsize=8, uniformtext_mode='hide',font=dict(color="black"),
xaxis={'categoryorder':'category ascending'})
fig.show()
In [29]:
fig = px.box(df1, x="Methodology",y="# of Polls",range_y=[-1,200],color="Methodology",
title='NUMBER OF POLLS vs METHODOLOGY')
fig.update_layout({'plot_bgcolor': 'rgb(255,255,255)','paper_bgcolor': 'rgb(255,20,147)'},
uniformtext_minsize=8, uniformtext_mode='hide',font=dict(color="white"),
xaxis={'categoryorder':'category ascending'})
fig.show()
OBSERVATION -->From these plottings methodologies a pollster routinely uses in its election polls in campaign cycles
is moslty "Interactive voice response" and "Live telephone interviews, including cellphones", and it had outliers, "Methodologies" survey not conducted in "2014".
---------------------------------------------------------------------------------------------------------------¶
5. What is polling firm usage rate from "Banned by FiveThirtyEight's election" ?
In [30]:
fig = px.bar(df1, x="Banned by 538", y="# of Polls", color="Banned by 538",barmode="stack",
animation_frame="# of Polls",animation_group="Banned by 538",range_y=[0,777],hover_name="Year"
,title='NUMBER OF POLLS vs BANNED BY 538',text="# of Polls")
fig.update_layout({'plot_bgcolor': 'rgb(0,0,0)','paper_bgcolor': 'rgb(218,165,32)'},
uniformtext_minsize=8, uniformtext_mode='hide',
font=dict(color="white"),
xaxis={'categoryorder':'category ascending'})
fig.update_traces(texttemplate='%{text:.3s}', textposition='outside')
fig.show()
In [31]:
fig = px.scatter(df1, x="Banned by 538", y="# of Polls", color="Banned by 538",
animation_frame="# of Polls",size_max=100,hover_name="Year",
animation_group="Banned by 538",range_y=[0,777],size="# of Polls",
title='NUMBER OF POLLS vs BANNED BY 538')
fig.update_layout({'plot_bgcolor': 'rgb(216,191,216)','paper_bgcolor': 'rgb(139,0,139)'},
uniformtext_minsize=8, uniformtext_mode='hide',font=dict(color="white"),
)
fig.show()
In [32]:
fig = px.histogram(df1, x="Banned by 538", y="# of Polls",color="Banned by 538",
animation_frame="# of Polls",animation_group="Banned by 538",range_y=[0,777],
title='NUMBER OF POLLS vs BANNED BY 538')
fig.update_layout({'plot_bgcolor': 'rgb(0,255,0)','paper_bgcolor': 'rgb(255,215,0)'},
uniformtext_minsize=8, uniformtext_mode='hide',font=dict(color="black"),
xaxis={'categoryorder':'category ascending'})
fig.show()
In [33]:
fig = px.density_heatmap(df1, x="Banned by 538",y="# of Polls",range_y=[0,150],
title='NUMBER OF POLLS vs BANNED BY 538')
fig.update_layout({'plot_bgcolor': 'rgb(255,0,255)','paper_bgcolor': 'rgb(127,255,212)'},
uniformtext_minsize=8, uniformtext_mode='hide',font=dict(color="black"),
xaxis={'categoryorder':'category ascending'})
fig.show()
In [34]:
fig = px.strip(df1, x="Banned by 538",y="# of Polls",range_y=[-1,300],hover_name="Year",color = "Banned by 538",
stripmode='overlay',title='NUMBER OF POLLS vs BANNED BY 538')
fig.update_layout({'plot_bgcolor': 'rgb(255,255,255)','paper_bgcolor': 'rgb(0,128,0)'},
uniformtext_minsize=8, uniformtext_mode='hide',font=dict(color="white"),
xaxis={'categoryorder':'category ascending'})
fig.show()
In [35]:
fig = px.box(df1, x="Banned by 538",y="# of Polls",range_y=[-1,135],color="Banned by 538",
title='NUMBER OF POLLS vs BANNED BY 538')
fig.update_layout({'plot_bgcolor': 'rgb(238,232,170)','paper_bgcolor': 'rgb(189,183,107)'},
uniformtext_minsize=8, uniformtext_mode='hide',font=dict(color="white"),
xaxis={'categoryorder':'category ascending'})
fig.show()
OBSERVATION -->From these plottings most of the response from "Banned by FiveThirtyEight's election" 708 polls "NO" so pollsters follows this 538's polling firm. and "Banned by FiveThirtyEight's election" action not started in "2014".
----------------------------------------------------------------------------------------------------------¶
6. Which "Grade" is getting highest number of polls ?
In [36]:
fig = px.bar(df1, x="538 Grade", y="# of Polls", color="538 Grade",barmode="stack",
animation_frame="# of Polls",animation_group="538 Grade",range_y=[0,777],hover_name="Year",
title='NUMBER OF POLLS vs 538 GRADE',text="# of Polls")
fig.update_layout({'plot_bgcolor':'rgb(0,0,0)','paper_bgcolor':'rgb(218,165,32)'},
uniformtext_minsize=8, uniformtext_mode='hide',font=dict(color="white"),
xaxis={'categoryorder':'category ascending'})
fig.update_yaxes(showgrid=True)
fig.update_traces(texttemplate='%{text:.3s}', textposition='outside')
fig.show()
In [37]:
fig = px.scatter(df1, x="538 Grade", y="# of Polls", color="538 Grade",
animation_frame="# of Polls",size_max=100,hover_name="Year",
animation_group="538 Grade",range_y=[0,777],size="# of Polls",
title='NUMBER OF POLLS vs 538 GRADE')
fig.update_layout({'plot_bgcolor': 'rgba(111, 882, 999, 100)','paper_bgcolor': 'rgba(222, 993, 1000, 200)'},
uniformtext_minsize=8, uniformtext_mode='hide',font=dict(color="black"))
fig.show()
In [38]:
fig = px.histogram(df1, x="538 Grade", y="# of Polls",color="538 Grade",
animation_frame="# of Polls",animation_group="538 Grade",range_y=[0,777],
title='NUMBER OF POLLS vs 538 GRADE')
fig.update_layout({'plot_bgcolor': 'rgb(244,164,96)','paper_bgcolor': 'rgb(245,222,179)'},
uniformtext_minsize=8, uniformtext_mode='hide',font=dict(color="black"),
xaxis={'categoryorder':'category ascending'})
fig.show()
In [39]:
fig = px.density_heatmap(df1, x="538 Grade",y="# of Polls",range_y=[0,150],
title='NUMBER OF POLLS vs 538 GRADE',hover_name = "Pollster")
fig.update_layout({'plot_bgcolor': 'rgb(255,0,255)','paper_bgcolor': 'rgb(127,255,212)'},
uniformtext_minsize=8, uniformtext_mode='hide',font=dict(color="black"),
xaxis={'categoryorder':'category ascending'})
fig.show()
In [40]:
from plotly import graph_objects as go
fig = go.Figure(layout={'plot_bgcolor':'skyblue','title':'NUMBER OF POLLS vs 538 GRADE'})
fig.add_trace(go.Funnel(
name = "'C' Grade",
orientation = "h",
y = ["C","C+","C-"],
x = [1193,669,433],marker={'color':['#FF1493','#FFB6C1','#FAEBD7']},
textposition = "inside",
textinfo = "value"))
fig.add_trace(go.Funnel(
name = "'B' Grade",
orientation = "h",
y = ["B+","B","B-"],
x = [794,481,367],marker={'color':['#228B22','#32CD32','#98FB98']},
textposition = "inside",
textinfo = "value"))
fig.add_trace(go.Funnel(
name = "'A' Grade",
orientation = "h",
y = ["A","A-","A+"],
x = [797,332,72],marker={'color':['#B8860B','#DAA520','#EEE8AA']},
textposition = "inside",
textinfo = 'value'))
fig.add_trace(go.Funnel(
name = "'D' Grade",
orientation = "h",
y = ["D+","D","D-"],
x = [142,104,10],marker={'color':['#00CED1','#00FFFF','#AFEEEE']},
textposition = "inside",
textinfo = 'value'))
fig.add_trace(go.Funnel(
name = "'F' Grade",
orientation = "h",
y = ["F"],
x = [219],marker={'color':['#FF0000']},
textposition = "inside",
textinfo = 'value'))
fig.update_layout(height=650,width=1000,font=dict(color="black",size = 15))
fig.show()
In [41]:
fig = px.box(df1, x="538 Grade",y="# of Polls",range_y=[-1,150],color = "538 Grade",
title='NUMBER OF POLLS vs 538 GRADE')
fig.update_layout({'plot_bgcolor': 'rgb(0,0,0)','paper_bgcolor': 'rgb(192,192,192)'},
uniformtext_minsize=8, uniformtext_mode='hide',font=dict(color="black"),
xaxis={'categoryorder':'category ascending'})
fig.show()
In [42]:
fig = px.bar(df1, x="538 Grade", y="# of Polls",hover_name="Year",color = "Year",
range_y=[0,3500])
fig.update_layout({'plot_bgcolor': 'rgba(111, 882, 999, 100)','paper_bgcolor': 'rgba(222, 993, 1000, 200)'}
,font=dict(color="black"),yaxis=dict(showticklabels=False))
fig.show()
OBSERVATION --> From these plottings "A Grade" is got highest number of polls (777) in Year = 2020. but most of the pollsters are got "C+ Grade"
---------------------------------------------------------------------------------------------------------------¶
7. How many polls are occured by "Year" wise response in "538 Grade","NCPP / AAPOR / Roper","Exclusively Live Caller With Cellphones","Methodology","Banned by 538"
In [43]:
sns.set(rc={'axes.facecolor':'w', 'figure.facecolor':'gold'})
fig, axes = plt.subplots(5,figsize=(18,25))
sns.barplot(x=df1["538 Grade"],y=df1["# of Polls"],hue=df1["Year"],ax = axes[0],palette="Set1")
sns.barplot(x=df1["NCPP / AAPOR / Roper"],y=df1["# of Polls"],hue=df1["Year"],ax = axes[1],palette="Set1")
sns.barplot(x=df1["Exclusively Live Caller With Cellphones"],y=df1["# of Polls"],hue=df1["Year"],ax = axes[2],palette="Set1")
sns.barplot(x=df1["Methodology"],y=df1["# of Polls"],hue=df1["Year"],ax = axes[3],palette="Set1")
sns.barplot(x=df1["Banned by 538"],y=df1["# of Polls"],hue=df1["Year"],ax = axes[4],palette="Set1")
plt.show()
In [44]:
sns.set(rc={'axes.facecolor':'w', 'figure.facecolor':'lightgreen'})
fig, axes = plt.subplots(5,figsize=(18,25))
sns.lineplot(x=df1["538 Grade"],y=df1["# of Polls"],hue=df1["Year"],ax = axes[0],palette="Set1",style = df1["Year"],
markers=True, dashes=False, lw=1)
sns.lineplot(x=df1["NCPP / AAPOR / Roper"],y=df1["# of Polls"],hue=df1["Year"],ax = axes[1],palette="Set1",style = df1["Year"]
,markers=True, dashes=False, lw=3)
sns.lineplot(x=df1["Exclusively Live Caller With Cellphones"],y=df1["# of Polls"],hue=df1["Year"],ax = axes[2],palette="Set1",
style = df1["Year"],markers=True, dashes=False, lw=3)
sns.lineplot(x=df1["Methodology"],y=df1["# of Polls"],hue=df1["Year"],ax = axes[3],palette="Set1",style = df1["Year"],
markers=True, dashes=False, lw=3)
sns.lineplot(x=df1["Banned by 538"],y=df1["# of Polls"],hue=df1["Year"],ax = axes[4],palette="Set1",style = df1["Year"],
markers=True, dashes=False, lw=3)
plt.show()
In [45]:
sns.set(rc={'axes.facecolor':'w', 'figure.facecolor':'lightpink'})
fig, axes = plt.subplots(5,figsize=(18,25))
sns.pointplot(x=df1["538 Grade"],y=df1["# of Polls"],hue=df1["Year"],ax = axes[0],palette="Set1",
ci=25,capsize=.1,markers = '^')
sns.pointplot(x=df1["NCPP / AAPOR / Roper"],y=df1["# of Polls"],hue=df1["Year"],ax = axes[1],palette="Set1",
ci=25,capsize=.1,markers = '*')
sns.pointplot(x=df1["Exclusively Live Caller With Cellphones"],y=df1["# of Polls"],hue=df1["Year"],ax = axes[2],palette="Set1",
ci=25,capsize=.1,markers = 'D')
sns.pointplot(x=df1["Methodology"],y=df1["# of Polls"],hue=df1["Year"],ax = axes[3],palette="Set1",
ci=25,capsize=.1,markers = 's')
sns.pointplot(x=df1["Banned by 538"],y=df1["# of Polls"],hue=df1["Year"],ax = axes[4],palette="Set1",
ci=25,capsize=.1,markers = '+')
plt.show()
In [46]:
sns.set(rc={'axes.facecolor':'w', 'figure.facecolor':'gold'})
fig, axes = plt.subplots(5,figsize=(18,25))
sns.boxenplot(x=df1["538 Grade"],y=df1["# of Polls"],hue=df1["Year"],ax = axes[0],palette="Set1")
sns.boxenplot(x=df1["NCPP / AAPOR / Roper"],y=df1["# of Polls"],hue=df1["Year"],ax = axes[1],palette="Set1")
sns.boxenplot(x=df1["Exclusively Live Caller With Cellphones"],y=df1["# of Polls"],hue=df1["Year"],ax = axes[2],palette="Set1")
sns.boxenplot(x=df1["Methodology"],y=df1["# of Polls"],hue=df1["Year"],ax = axes[3],palette="Set1")
sns.boxenplot(x=df1["Banned by 538"],y=df1["# of Polls"],hue=df1["Year"],ax = axes[4],palette="Set1")
plt.show()
OBSERVATION -->From these plottings In 538 Grade "A and F" got highest no' of polls in "2014" arround '150' and it had outliers.
In NCPP/AAPOR/Roper(Public endorsement)"Yes" had "45 polls" in "2020",42 polls in 2016,40 polls in 2014
In Exclusively Live Caller With Cellphones "150 polls" occured in 2014 that is "Sometimes".
In Methodology "IVR,LIVE,ONLINE" got arround "250 polls" only in year 2020.
In Banned by 538 "Yes" got "150 polls in 2014","100 polls in 2016" and "80 polls in 2014"
-------------------------------------------------------------------------------------------------------------¶
8. which "pollster" is most "biased" in which "Year" ?
In [47]:
fig = px.bar(df1,x="Pollster",y ="# of Polls for Bias Analysis",color = "Pollster",
animation_frame="# of Polls for Bias Analysis",animation_group="Pollster",hover_name="Year",
title='POLLS FOR BIAS ANALYSIS vs POLLSTER corresponding YEAR',text="# of Polls for Bias Analysis",
range_y=[0,652])
fig.update_layout({'plot_bgcolor': 'rgb(0,255,255)','paper_bgcolor': 'rgb(245,222,179)'},
uniformtext_minsize=8, uniformtext_mode='hide',font=dict(color="black"),
xaxis={'categoryorder':'category ascending'})
fig.show()
In [48]:
fig = px.pie(df1.query("Year == 2020"), values='# of Polls for Bias Analysis', names='Pollster',hover_name="Year",
hole=.5,title='POLLS FOR BIAS ANALYSIS vs POLLSTER corresponding YEAR --> 2020')
fig.update_traces(textposition='inside')
fig.update_layout({'paper_bgcolor': 'rgb(0,255,0)'},uniformtext_minsize=10,
uniformtext_mode='hide',font=dict(color="Black"))
fig.show()
In [49]:
fig = px.pie(df1.query("Year == 2016"), values='# of Polls for Bias Analysis', names='Pollster',hover_name="Year",
hole=.5,title='POLLS FOR BIAS ANALYSIS vs POLLSTER corresponding YEAR --> 2016')
fig.update_traces(textposition='inside')
fig.update_layout({'paper_bgcolor': 'rgb(192,192,192)'},uniformtext_minsize=10,
uniformtext_mode='hide',font=dict(color="white"))
fig.show()
OBSERVATION --> From these plottings in Bias Analysis the most biased pollster is "YouGov" in "2016" polls "652" and "Bias Analysis" not done in "2014".
---------------------------------------------------------------------------------------------------¶
9. What is range between AVERAGE & EXPECTED error ?
In [50]:
fig = px.density_heatmap(df1, x="Simple Average Error", y="Simple Expected Error",
marginal_x="box", marginal_y="histogram",range_x=[0,25],hover_data=['Year','Pollster'],
title="AVERAGE ERROR & EXPECTED ERROR in YEAR")
fig.update_layout({'paper_bgcolor': 'rgb(0,255,255)'},
uniformtext_minsize=8, uniformtext_mode='hide',font=dict(color="black"),
xaxis={'categoryorder':'category ascending'},width = 1000)
fig.show()
In [51]:
fig = px.scatter(df1.query("Year != 2014"), x="Simple Average Error", y="Simple Expected Error",
title="AVERAGE ERROR & EXPECTED ERROR in YEAR",hover_data=["Year","Pollster"],
marginal_x="box", marginal_y="histogram",range_x=[0,25],range_y=[2,10])
fig.update_traces(marker=dict(size=12,
line=dict(width=2,
color='DarkSlateGrey')),
selector=dict(mode='markers'))
fig.update_layout({'paper_bgcolor': 'rgb(255,0,0)'},
uniformtext_minsize=8, uniformtext_mode='hide',font=dict(color="white"),
xaxis={'categoryorder':'category ascending'},width = 950,height = 500)
fig.show()
OBSERVATION --> From these plottings here maximum density of error occured between 4.0 - 6.0 so the margin difference between firm's average error & the expected error for the races the firm surveyed is "1"
-----------------------------------------------------------------------------------------------------¶
10. What is year wise response in "PREDICTIVE & ADVANCED PLUS MINUS" ?
In [52]:
fig = px.histogram(df1, x="Predictive Plus-Minus", y="Advanced Plus-Minus", color="Year",
marginal="box",title="PREDICTIVE & ADVANCED PLUS MINUS in YEAR")
fig.update_layout({'plot_bgcolor': 'rgb(224,255,255)','paper_bgcolor': 'rgb(255,215,0)'},
uniformtext_minsize=8, uniformtext_mode='hide',font=dict(color="black"),
xaxis={'categoryorder':'category ascending'},width = 1000,height = 600)
fig.show()
In [53]:
fig = px.density_contour(df1, x="Predictive Plus-Minus", y="Advanced Plus-Minus", color="Year",
marginal_x="box", marginal_y="histogram",
range_x=[-1,1.5],range_y = [-10,10],
title="PREDICTIVE & ADVANCED PLUS MINUS in YEAR")
fig.update_layout({'plot_bgcolor': 'rgb(255,255,255)','paper_bgcolor': 'rgb(255,20,147)'},
uniformtext_minsize=8, uniformtext_mode='hide',font=dict(color="white"),
xaxis={'categoryorder':'category ascending'},width = 1000)
fig.show()
In [54]:
fig = px.density_heatmap(df1, x="Predictive Plus-Minus", y="Advanced Plus-Minus",
marginal_x="box", marginal_y="histogram",color_continuous_scale=px.colors.sequential.Cividis_r,
range_x=[-1,1.5],range_y = [-10,10],hover_data=["Year","# of Polls","Methodology"],
title="PREDICTIVE & ADVANCED PLUS MINUS in YEAR" )
fig.update_layout({'plot_bgcolor': 'rgb(128,0,128)','paper_bgcolor': 'rgb(0,128,0)'},
uniformtext_minsize=8, uniformtext_mode='hide',font=dict(color="white"),
xaxis={'categoryorder':'category ascending'},width = 980)
fig.show()
In [55]:
sns.set(rc={'axes.facecolor':'cyan', 'figure.facecolor':'khaki'})
fig, axes = plt.subplots(1,2,figsize=(20 ,8))
sns.boxplot(x=df1["Year"],y=df1["Predictive Plus-Minus"],ax = axes[0],palette="Set1")
sns.boxplot(x=df1["Year"],y=df1["Advanced Plus-Minus"],ax = axes[1],palette="Set1")
Out[55]:
OBSERVATION --> From these plottings the maximum density of plus minus values occuring range is (0-2),
Negative scores are favorable and indicate above-average quality.(ie) "Methodology"--> "Live", and Advanced & Predictive Plus-Minus scores had outliers, in 2014 both Advanced & Predictive Plus-Minus scores have high methodological quality
----------------------------------------------------------------------------------------------¶
11. What is year wise response in "SIMPLE & Mean-Reverted ADVANCED PLUS MINUS" ?
In [56]:
fig = px.histogram(df1.query("Year != 2016"), x="Simple Plus-Minus",y = "Mean-Reverted Advanced Plus Minus",
color="Year", marginal="box",
hover_data=["Year","Advanced Plus-Minus","Mean-Reverted Advanced Plus Minus","Predictive Plus-Minus"],
range_x=[-10,20],
title="SIMPLE & MEAN REVERTED ADVANCED PLUS MINUS in YEAR" ,
color_discrete_sequence= px.colors.sequential.Plasma_r,)
fig.update_layout({'plot_bgcolor': 'rgb(0,0,0)','paper_bgcolor': 'rgb(0,250,154)'},
uniformtext_minsize=8, uniformtext_mode='hide',font=dict(color="black"),
xaxis={'categoryorder':'category ascending'},width = 980,height = 600)
fig.show()
In [57]:
fig = px.scatter(df1.query("Year != 2016"), y="Simple Plus-Minus",x = "Mean-Reverted Advanced Plus Minus",
size="Year", size_max=25,color = "Year",marginal_x="box",marginal_y="violin",
hover_data=["Year","Advanced Plus-Minus","Mean-Reverted Advanced Plus Minus","Predictive Plus-Minus"],
color_discrete_sequence= px.colors.sequential.Tealgrn_r,
title="SIMPLE & MEAN REVERTED ADVANCED PLUS MINUS in YEAR")
fig.update_traces(marker=dict(size=22,
line=dict(width=2,
color='DarkSlateGrey')),
selector=dict(mode='markers'))
fig.update_layout({'plot_bgcolor': 'rgb(245,255,250)','paper_bgcolor': 'rgb(128,0,0)'},
uniformtext_minsize=8, uniformtext_mode='hide',font=dict(color="White"),
xaxis={'categoryorder':'category ascending'},width = 1080,height = 600)
fig.show()
OBSERVATION -->From these plottings the density of values occuring in betweeen (-10 to 15) & (-1 to 1) in 2020, negative scores are favorable and indicate above-average quality. and both "SIMPLE & MEAN REVERTED ADVANCED PLUS MINUS" having outliers in 2020,2014, "SIMPLE & MEAN REVERTED ADVANCED PLUS MINUS scores" results not occur in "2016".
----------------------------------------------------------------------------------------------------¶
12. Which pollster is having maximum and minimum Republican,Democratic scores in year 2020( BIAS & HOUSE EFFECT )?
In [58]:
fig = px.density_contour(df1.query("Year == 2020"),x = "Bias",y = "House Effect",range_x=[-10,10],
animation_frame="Pollster",range_y=[-2.5,5.0],hover_data=["Bias","House Effect"],
title="BIAS & HOUSE EFFECT in YEAR --> 2020",
color="Pollster")
fig.update_traces(line_width=2)
fig.update_layout({'plot_bgcolor': 'rgb(255,255,255)','paper_bgcolor': 'rgb(255,69,0)'},
uniformtext_minsize=8, uniformtext_mode='hide',font=dict(color="white"),
xaxis={'categoryorder':'category ascending'},width = 980)
fig.show()
In [59]:
fig = px.density_heatmap(df1.query("Year == 2020"),x = "Bias",y = "House Effect",range_x=[-25,20],
range_y=[-20,20],hover_data=["Bias","House Effect"],
color_continuous_scale=px.colors.sequential.Viridis_r,
title="BIAS & HOUSE EFFECT in YEAR --> 2020" )
fig.update_layout({'paper_bgcolor': 'rgb(255,20,147)'},
uniformtext_minsize=8, uniformtext_mode='hide',font=dict(color="white"),width = 980)
fig.show()
In [60]:
fig = px.histogram(df1.query("Year == 2020"),x = "Bias",y = "House Effect",color="Year",
marginal="box",color_discrete_sequence=px.colors.sequential.Cividis_r,range_x=[-25,25],
title="BIAS & HOUSE EFFECT in YEAR --> 2020")
fig.update_layout({'plot_bgcolor': 'rgb(0,0,0)','paper_bgcolor': 'rgb(0,250,154)'},showlegend=False,
uniformtext_minsize=8, uniformtext_mode='hide',font=dict(color="black"),
xaxis={'categoryorder':'category ascending'},width = 980,height = 600,
)
fig.show()
OBSERVATION From these plottings BIAS in "2020": Republican score is "-20" ,pollster is "Massive & Associates" Democratic score is "40",pollster is "Jayhawk consulting"
HOUSE EFFECT in "2020": Republican score is "-25" ,pollster is "Fort Hays State University" Democratic score is "30",pollster is "Riggs Research Services"
-------------------------------------------------------------------------------------------------¶
13. What is the "MARGIN OF ERROR" response in year(2020) of pollsters?
In [61]:
fig = px.scatter(df1.query("Year == 2020"),x="Pollster",y="Misses Outside MOE",color="Pollster",
marginal_x="rug",marginal_y = "histogram",
title = "MISSES OUTSIDE MOE vs POLLSTER in YEAR --> 2020")
fig.update_traces(marker=dict(size=25,line=dict(width=1,color='DarkSlateGrey')),
selector=dict(mode='markers'))
fig.update_layout({'plot_bgcolor': 'rgb(245,255,250)','paper_bgcolor': 'rgb(255,215,0)'}
,font=dict(color="black"),xaxis=dict(showticklabels=False)
,width = 980,height = 550,showlegend = False)
fig.show()
In [62]:
fig = px.bar(df1.query("Year == 2020"),x="Pollster",y="Misses Outside MOE", color="Pollster",barmode="group",
animation_frame="Misses Outside MOE",animation_group="Pollster",hover_name="Year",range_y=[0,1],
title='MISSES OUTSIDE MOE vs POLLSTER in YEAR --> 2020',text="Misses Outside MOE")
fig.update_layout({'plot_bgcolor': 'rgb(238,232,170)','paper_bgcolor': 'rgb(218,165,32)'},
uniformtext_minsize=8, uniformtext_mode='hide',font=dict(color="black"),
xaxis={'categoryorder':'category ascending'})
fig.update_traces(texttemplate='%{text:1%s}', textposition='outside')
fig.show()
OBSERVATION --> From these plottings most of the pollsters got (0 - 50%) error. And this poll's margin of error percentage is in increasing order.
------------------------------------------------------------------------------------------------------------¶
-CONCLUSION-
FROM ALL THESE VISUALIZATIONS WE DECLARE THE POLLSTER IS "Rasmussen Reports/Pulse Opinion Research" ORAGANISATION HAD HIGHEST POLL RATING SO IT WILL GIVES THE BEST POLL CANDIDATE WINNING PREIDICTION.
In [ ]: